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Article

Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks

1
Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, FI-33720 Tampere, Finland
2
Electrical Engineering Department, College of Engineering, Qatar University, Doha QA-2713, Qatar
3
Electrical and Electronics Engineering Department, Izmir University of Economics, Izmir TR-35330, Turkey
*
Author to whom correspondence should be addressed.
Remote Sens. 2019, 11(11), 1340; https://doi.org/10.3390/rs11111340
Received: 3 May 2019 / Revised: 31 May 2019 / Accepted: 2 June 2019 / Published: 4 June 2019
Accurate land use/land cover classification of synthetic aperture radar (SAR) images plays an important role in environmental, economic, and nature related research areas and applications. When fully polarimetric SAR data is not available, single- or dual-polarization SAR data can also be used whilst posing certain difficulties. For instance, traditional Machine Learning (ML) methods generally focus on finding more discriminative features to overcome the lack of information due to single- or dual-polarimetry. Beside conventional ML approaches, studies proposing deep convolutional neural networks (CNNs) come with limitations and drawbacks such as requirements of massive amounts of data for training and special hardware for implementing complex deep networks. In this study, we propose a systematic approach based on sliding-window classification with compact and adaptive CNNs that can overcome such drawbacks whilst achieving state-of-the-art performance levels for land use/land cover classification. The proposed approach voids the need for feature extraction and selection processes entirely, and perform classification directly over SAR intensity data. Furthermore, unlike deep CNNs, the proposed approach requires neither a dedicated hardware nor a large amount of data with ground-truth labels. The proposed systematic approach is designed to achieve maximum classification accuracy on single and dual-polarized intensity data with minimum human interaction. Moreover, due to its compact configuration, the proposed approach can process such small patches which is not possible with deep learning solutions. This ability significantly improves the details in segmentation masks. An extensive set of experiments over two benchmark SAR datasets confirms the superior classification performance and efficient computational complexity of the proposed approach compared to the competing methods. View Full-Text
Keywords: Convolutional Neural Networks; synthetic aperture radar (SAR); land use/land cover classification; sliding window Convolutional Neural Networks; synthetic aperture radar (SAR); land use/land cover classification; sliding window
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MDPI and ACS Style

Ahishali, M.; Kiranyaz, S.; Ince, T.; Gabbouj, M. Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks. Remote Sens. 2019, 11, 1340. https://doi.org/10.3390/rs11111340

AMA Style

Ahishali M, Kiranyaz S, Ince T, Gabbouj M. Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks. Remote Sensing. 2019; 11(11):1340. https://doi.org/10.3390/rs11111340

Chicago/Turabian Style

Ahishali, Mete; Kiranyaz, Serkan; Ince, Turker; Gabbouj, Moncef. 2019. "Dual and Single Polarized SAR Image Classification Using Compact Convolutional Neural Networks" Remote Sens. 11, no. 11: 1340. https://doi.org/10.3390/rs11111340

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